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Wearing personal safety protective equipment (PSPE) plays a key role in reducing electrical injuries to electrical workers. However, due to the lack of safety awareness, operators often do not wear PSPE when carrying out inspection or maintenance projects in substations, which is the main reason for personal injury accidents. Therefore, it is necessary to detect the wearing of PSPE in real-time through a video surveillance system. In this paper, a wear-enhanced YOLOv3 method for real-time detection of PSPE wear of substation operators is proposed. In order to improve the detection accuracy, the gamma correction is applied as the preprocessing method to highlight the details of the operators. Besides, K-means++ algorithm is introduced to get the most suitable prior bounding box size to improve the detection speed. Based on the proposed method, it can quickly and effectively detect whether the substation operators are wearing safety helmets and insulating gloves and boots correctly. Finally, extensive experiments are carried out using a dataset of real substation monitoring images to illustrate the effectiveness of the proposed method for realtime PSPE wear detection.

The fourth industrial revolution-Industry 4.0-puts emphasis on the application of intelligent technologies in the area of monitoring and identification of electrical equipment. High precision and non-contact qualities make the infrared thermography one of the most suitable technologies for intelligent inspection of high-voltage apparatus. Yet, due to imperfect data acquisition methods and difficulties in collecting data, electrical equipment images are limited in quantities and imbalanced in representing different types of devices. Additionally, it is not easy to extract representative features of infrared images due to their low-intensity contrast and uneven distribution. In this paper, a data-driven framework is proposed for the identification of electrical equipment based on infrared images. The main technique of this proposed system is a novel process of generating synthetic infrared images. For this purpose, an Edge-Oriented Generative Adversarial Network (EOGAN) is developed. The EOGAN is designed to create realistic infrared images that can be used as augmented data for developing data-driven identification methods. Extracted edge features of electrical equipment are utilized as prior information to guide the process of generating realistic infrared images. Finally, comparative experiments are carried out to show the effectiveness of the proposed EOGAN-based framework for equipment identification in the presence of limited and imbalanced image datasets. INDEX TERMS Edge prior knowledge, electrical equipment identification, generative adversarial network, infrared image.

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